Data Analysis

In this section, to build several data sets, the different types of experimental paradigms using only the training part of the interfaces described in Section 3.2.1 were designed. Afterwards, the data sets recorded in these experiments are divided into training and test data sets by applying two-fold cross validation. The offline data analysis results are fully detailed and explained.

Experiment Type I

In the experiment type I, the training part of the interface type I was used and the subjects were asked to imagine right or left arm imagery movements according to the cues shown on the screen. At the beginning of a trial, a cross ‘+” is displayed for 3 seconds then, a right or left arrow appears as a cue for 6 seconds. Therefore, the length of a trial is 9 seconds as shown in Figure 3.7. A run consists of 40 trials (20 trials for right/left movement) and an experiment consists of 3 or 4 runs to avoid fatigue. Six healthy subjects participated in this experiment. The signals were sampled by 2 kHz (2048 samples) and were downsampled to 512 Hz to reduce the amount of data to be processed.

Figure 3.7: Timing scheme of experiment Type I.

The recordings were done over 35 channels that are presented in red colour in the Figure 3.8. But all 35 channels do not give important information about the motor imagery movements. In the literature, it has been shown that ERD is spread mainly over the central areas which include the central (C3,Cz,C4), frontal (F C1,F C2) and

postcentral (CP1,CP 2) channels [12, 58, 59]. To find the most informative chan-

nels, the classification results of the features obtained from different channels (3 electrodes:C3, Cz, C4 and 7 electrodes: C3, Cz, C4, F C1, F C2, CP1, CP 2) with two

Figure 3.8: Positions of the electrodes used in our experiments.

In the first referencing method, 4 channels positioned around a channel (upper, lower, right, left neigbors) are used as the references of that main channel which is located at the center. This method is called the Laplace Method. The second referencing method is to take the upper and lower neighbors (anterior and posterior) of a main channel as its references. The mean of the data acquired from these references is subtracted from the main channel and a referenced channel is obtained. The averaged PSDs of specified frequency bands (alpha, sigma and beta) were selected as features. LDA and SVM were used as classification methods.

To eliminate data recorded before the subject has had enough time to concentrate on the task, the first trial of each run was eliminated. Therefore, 117 or 156 trials are obtained from one subject in 3 or 4 runs. The performance of the classifier was measured by applying two-fold cross validation for 100 times to obtain different training and test datasets consisting of the 75% and the 25% of the entire data set, respectively. Overall classification accuracy was obtained by averaging over these 100 classification results (see Figure 3.9). The mean accuracies across the subjects are presented in Figure 3.10. The averaged LDA accuracies of 3 electrodes with 2

references and 7 electrodes with 4 references are close to each other. If we make the recordings over many channels, then the amount of the data will be increased and this will increase the analysis time. Therefore, we may say that the EEG signals which are measured over 3 channels with 2 references is sufficient for the LDA classifier.

Figure 3.9: Classification results of experiment type I.

Figure 3.10: The mean classification accuracies across the subjects of experiment type I.

Experiment Type II

In the experiment type II, the training part of the interface type II was used. The data set of this experiment was recorded from three healthy subjects. While subjects sat quietly during data collection, without visible arm movements, their task was

to close their eyes for resting or to imagine right arm movements. A run consists of 60 trials (30 trials for right arm imagery movement and 30 trials for resting) and an experiment consists of 3 or 4 runs to avoid the fatigue. “+” is displayed for 3 seconds then, a right arrow or “Relax” appears as a cue for 6 seconds. Therefore, the length of a trial is 9 seconds as shown in Figure 3.11. The signals were sampled by 2 kHz (2048 samples) and were downsampled to 512 Hz. To eliminate data recorded before the subject has had enough time to concentrate on the task the first trial of each run was eliminated. Therefore, 177 or 236 trials are obtained from one subject in 3 or 4 runs.

Period with Cue

0 1 2 3 4 5 6 7 8 9

Figure 3.11: Timing scheme of experiment Type II.

The recording configuration shown in Figure 3.12 uses Ag-Cl electrodes at C3,

Cz, C4 locations of the international 10-20 electrode placement system, at 512 Hz

sampling rate. Their anterior and posterior channels are used as references. By subtracting the average of the data received from upper and lower neighbor channels of a main channel, three referenced main channels are obtained.

The averaged PSDs in a specified timing window for each subject were classified by LDA and SVM. The performance of the classifiers was measured by applying two-fold cross validation for 100 times to obtain different training and test datasets consisting of the 75% and the 25% of the entire data set, respectively. Overall classification accuracy was obtained by averaging over these 100 classification results (see Figure 3.13). The results are greater than 90% for Subject 1 and Subject 2 where as Subject 3’s performance is greater than 70%.

Figure 3.13: Classification results of right arm imagery movement or closed eyes.

Experiment Type III

In the experiment type III, the interface type II was used. To examine if closing eyes in resting periods affects the performance of the classifier, in this experiment, subjects were asked to rest without closing their eyes or to imagine right arm move- ments. In resting periods, the subjects were asked to focus on the cue shown on the screen and just relax. The data set of this experiment was recorded from nine healthy subjects. The recording configuration uses Ag-Cl electrodes at C3, Cz, C4

locations and averaged PSDs of the alpha, sigma and beta frequency bands in a specified timing window for each subject were used as the features.

The performance of the LDA classifier was measured by applying two-fold cross validation for 300 times to obtain different training and test datasets consisting of the 75% and the 25% of the entire data, respectively. Overall classification accuracy

was obtained by averaging over these 300 classification experiments. Classification accuracy values vary between 84% and 63% across the subjects (see Table 3.1). The results show that the performance of motor imagery movement based BCIs, depend on the subject, his/her fatigue level and concentration. The level of accuracy we obtain is comparable to results reported in the BCI literature.

Subject No Accuracy (%) 1 70.1622 2 83.9504 3 64.0922 4 69.5714 5 70.3694 6 63.0357 7 79.8810 8 74.6047 9 72

Table 3.1: LDA classification accuracies.